The present invention relates to the production of animal feed and in particular to the selection and enhancement of raw materials for use in animal feed production.
Animal feeds typically consist of a mixture of materials. For instance, a typical composition for a feed for poultry is 25% soyabean meal, 50% corn, 20% byproducts suitable for animal feed and 5% minerals, vitamins, supplements and other feed additives. Feeds for other animals have different compositions, and soyabean meal is one of the most important vegetable protein sources for animal feeds in general. For instance, it is one of the main components of feed for poultry.
In order to achieve the most efficient growth of animals, the diet needs to be carefully controlled and thus the nutrient composition of the feedstuff is of high importance. However, natural raw materials have a high variation in nutrient composition. For instance, soyabean meal is generally classified into high and low protein products, namely high protein soyabean meal (HPSBM) with 49% protein and low protein soyabean meal (LPSBM) with 44% protein. High protein soyabean meal is of a higher price than low protein soyabean meal but in reality neither have a fixed level of protein, but vary within certain tolerance limits about the average value. The price of a raw materials such as soyabean meal is also variable; however, the prices among raw materials are often highly correlated because they may be a function of nutrient composition. In addition, soyabean meal also provides protein to the diet and the comprising amino acids and other nutrients. These other nutrients are very important in obtaining optimal performance of the feed, but the amount of the amino acids, respectively the first limiting amino acids: lysine, methionine, threonine and tryptophane, vary widely between different batches of soyabean meal. For example, the coefficient of variation (CV) of lysine and methionine composition amongst batches of soyabean meal can be approximately 10%.
In order to guarantee the level of desired nutrients in the feed, it has been proposed to measure the level of nutrient in raw material for the feed, and to supplement that level where necessary with additional components, such as synthetic nutrients. For instance, synthetic methionine can be added to the diets containing soyabean meal. In the case of the amino acid levels in soyabean meal, these can be assessed by inspecting the near infrared spectrum of the soyabean meal. It has been found that the near infrared spectrum (NIRS) of soyabean meal is dependent upon the amino acid content. By establishing a database relating the NIR spectrum to the amino acid levels (measured by other means), it is possible to use the NIR spectrum of a given batch of soyabean meal to assess its amino acid content.
Such techniques are described in xe2x80x9cNear-Infrared Reflectance Spectroscopy in Precision Feed Formulationxe2x80x9d by Van Kempen and Simmins; Applied Poultry Science, 1997, pp 471-475 and xe2x80x9cNIRS May Provide Rapid Evaluation of Amino Acidsxe2x80x9d by Van Kempen and Jackson, Feedstuffs, Dec. 2, 1996.
The known method may be applied to any of a number of feedstuffs and their comprising nutrients. For example, the caloric content, and specifically the metabolizable energy content of corn or the fat composition of bakery by-product meal, or the amino acid and caloric content of animal by-products, are examples to which the method applies.
A more typical way of providing a guaranteed level of nutrient is to assess the natural variation of the level of nutrient in the raw material and to add a sufficient amount of supplement to all batches of the raw material to achieve a guaranteed high level. Clearly this techniques does not reduce the natural variation in level, but raises the average level to the high level. However the content of nutrient in the diet containing this raw material may still be overestimated, the requirements of the animals are not met and the performance is reduced. To minimise this risk safety margins are used for the formulation of the feed. These safety margins result in a systematic overformulation of specific nutrient ingredients and such overformulation is costly and reduces efficiency.
During an analysis of the economic variability of soyabean meal and the variability of amino acids among batches of soyabean meal, an unexpected discovery was made. Whereas it was possible to guarantee a high level of a specific nutrient (e.g. methionine) among all of the batches by setting a high specification and supplementing (with synthetic methionine), the price of the enhanced raw material was limited vis-a-vis the price of comparable raw materials, especially soyabean meal products and other protein-providing raw materials. In fact, if a feedmill had NIR and supplemental methionine, the enhanced product by this method provided no economic advantage. However, it was discovered that certain clusters of soyabean meal could be identified farther up the supply chain (e.g. at a soy crusher) that had certain nutritional profiles that made these batches consistently favorable to feed formulation software that chooses the optimal raw materials to have in inventory and to be included in the feed formulations at that mill. These clusters were systematically undervalued if one looked only at the expected profile of nutrient composition rather than the measured values, in this case with NIRS, and the relative proportions of nutrients with respect to the specifications among all of the feed formulas produced by the feedmill. Furthermore, a threshold value could be determined for which minimal supplementation of the desired nutrients was necessary, for which enough product could be manufactured to meet anticipated demand for this product, and for which the guaranteed nutrient profile was higher in value than the price of supplement and raw material. In this example, total methionine and lysine, digestible methionine and lysine, and total protein could be measured in batches of soyabean meal with NIRS, clusters were discovered which had relative proportions of these nutrients close to a pre-determined threshold value, minimal supplementation of only synthetic methionine were needed, and diets formulated with this enhanced product were lower in cost, lower in variability, and higher in digestibility than that which could be obtained by conventional feed formulation with existing raw materials.
The present invention provides a method of analyzing, selecting and enhancing raw materials for use in animal feed products in a manner which eliminates the systematic overformulation, while guaranteeing a desired level of nutrient in the supplemented product. The invention also provides a method of determining a threshold value while considering the objectives of economic as well as nutritional value.
In more detail, according to one aspect, the present invention provides a method comprising the steps of:
analyzing the nutritional composition of batches of a raw material for use in an animal feed product;
comparing the nutritional composition with a predetermined nutritional composition;
calculating the amount of supplemental nutrient needed to bring the composition of the batch to the predetermined nutritional composition;
determining a threshold value for which clusters of the raw material exist that are both economically and nutritionally favorable;
screening the batches to reject those for which the amount of supplemental nutrient needed is greater than a threshold value and to accept those for which the amount of supplemental nutrient needed is less than a threshold value; and
supplementing only the accepted batches of raw material with the calculated amount of supplemental nutrient.
The method can further include a step of analyzing the statistical distribution (i.e. frequency) of the nutritional content of batches of the raw material, assessing the number of batches for which the amount of supplemental nutrient needed is less than the threshold value, and performing the screening and supplemental step on condition that the number of batches is greater than the predetermined value. This may conveniently be done by setting a nutritional composition threshold and comparing the nutritional content of each batch with the threshold, and rejecting those falling below the threshold.
The nutritional composition threshold may define the boundary of a range of nutritional compositions near to a predefined nutritional composition profile and the step of analyzing the statistical distribution of the nutritional content of the batches of the raw material may comprise estimating the percentage of batches that are clustered within that range.